Conventional magnetic resonance (MR) pulse sequences include a preparation phase, a waiting phase, and an acquisition phase that are configured to produce signals from which images can be made serially. The preparation phase determines when a signal can be acquired and determines the properties of the acquired signal. For example, a first pulse sequence may be designed to produce a T1-weighted signal at a first echo time (TE) while a second pulse sequence may be designed to produce a T2-weighted signal at a second TE. These conventional pulse sequences are typically designed to provide qualitative results where data are acquired with various weightings or contrasts that highlight a particular parameter (e.g., T1 relaxation, T2 relaxation).
When MR images are generated, they may be viewed by a radiologist and/or surgeon who interprets the qualitative images for specific disease signatures. The radiologist may examine multiple image types (e.g., T1-weighted, T2-weighted) acquired in multiple imaging planes to make a diagnosis. The radiologist or other individual examining the qualitative images may need particular skill to be able to assess changes from session to session, from machine to machine, and from machine configuration to machine configuration. Thus, the images are only as good as the image interpreter and all image based (e.g., qualitative) diagnoses end up being subjective.
Magnetic resonance fingerprinting (MRF) employs a series of varied sequence blocks that produce different signal evolutions in different resonant species (e.g., tissues) to which radio frequency (RF) energy is applied according to an MRF acquisition. The term “resonant species”, as used herein, refers to an item (e.g., water, fat, tissue, material) that can be made to resonate using NMR. By way of illustration, when example apparatus and methods apply RF energy to a volume that has both bone and muscle tissue, then both the bone and muscle tissue will produce an NMR signal. However the “bone signal” and the “muscle signal” will be different. The different signals can be collected over a period of time to identify a signal evolution for the volume. Resonant species in the volume can then be characterized by comparing the signal evolution to known evolutions. In one embodiment, the “known” evolutions may be, for example, simulated evolutions and/or previously acquired evolutions. MRF may store a large set of known evolutions in a dictionary. Characterizing the resonant species can include identifying different properties of a resonant species (e.g., T1, T2, diffusion resonant frequency, diffusion co-efficient, spin density, proton density). Additionally, other properties including, but not limited to, tissue types, materials, and super-position of attributes (e.g., T1, T2) can be identified. MRF is described in United States Patent Application “Nuclear Magnetic Resonance (NMR) Fingerprinting”, application Ser. No. 13/051,044, and in Magnetic Resonance Fingerprinting, Ma et al., Nature 495, 187-192 (14 Mar. 2013), the contents of both of which are incorporated herein by reference.
In MRF, characterizing the resonant species may be performed by comparing first information to second information. The first information may include the acquired NMR signals, the acquired signal evolution, or information derived from the acquired NMR signals or acquired signal evolution. The second information may include a stored signal evolution, a known signal evolution, a modeled signal evolution, information derived from stored signal evolutions, or information that is not a signal evolution. Both the first information and the second information may have a first high dimensionality. MRF may perform whole template matching that considers all the dimensions of the data. Conventionally, comparing the first information to the second information may be performed in various ways including, but not limited to, pattern matching, selection, minimization of a cost function, and optimization. The pattern matching may have been performed in a high dimensional space.
The result of the comparison may take different forms. In different embodiments, the result of the comparison may include, but is not limited to, an identification that the first information matches the second information, an identification that the first information matches the second information to within a tolerance, and an identification that there is a certain percent likelihood that the first information matches the second information. In other embodiments, the result of the comparison may include, but is not limited to, an identification of T1 for a resonant species, an identification of T2 for a resonant species, an identification of a diffusion coefficient, an identification of a spin density, an identification of a resonance frequency (e.g., chemical shift) and an identification of a proton density. In another embodiment, the comparison may include identifying the strength of a magnetic field (e.g., B0, B1) or may include identifying the strength of a gradient field. In yet another embodiment, the result of the comparison may identify a tissue type (e.g., brain, brain tumor) or may identify a material. Thus, the comparison may produce different results. In one embodiment, multiple results may be provided. For example, a weighted list of likely materials may be provided. In another example, multiple probabilities may be provided.
While conventional MRF has produced astounding improvements in MRI, performing full template matching between acquired signals and a conventional high dimension MRF dictionary may be computationally intensive, and thus may take a significant period of time. Improvements in processing time are constantly sought.